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In recent years there has been an increasing demand among home owners for cost effective sustainable energy production such as solar energy to provide heating and electricity. A lot of research has focused on the assessment of the incoming solar radiation on roof

In recent years there has been an increasing demand among home owners for cost effective sustainable energy production such as solar energy to provide heating and electricity. A lot of research has focused on the assessment of the incoming solar radiation on roof planes acquired by, e.g., Airborne Laser Scanning (ALS). However, solar panels can also be mounted on building facades in order to increase renewable energy supply. Due to limited reflections of points from vertical walls, ALS data is not suitable to perform solar potential assessment of vertical building facades. This paper focuses on a new method for automatic solar radiation modeling of facades acquired by Mobile Laser Scanning (MLS) and uses the full 3D information of the point cloud for both the extraction of vertical walls covered by the survey and solar potential analysis. Furthermore, a new method isintroduced determining the interior and exterior face, respectively, of each detected wall in order to calculate its slope and aspect angles that are of crucial importance for solar potential assessment. Shadowing effects of nearby objects are considered by computing the 3D horizon of each point of a facade segment within the 3D point cloud.
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The determination of the orthometric height from geometric leveling has practical difficulties that, despite a number of scientific and technological advances, passed a century without substantial modifications or advances. Currently, the Global Navigation Satellite System (GNSS) has been used with reasonable success for

The determination of the orthometric height from geometric leveling has practical difficulties that, despite a number of scientific and technological advances, passed a century without substantial modifications or advances. Currently, the Global Navigation Satellite System (GNSS) has been used with reasonable success for orthometric height determination. With a sufficient number of benchmarks with known horizontal and vertical coordinates, it is often possible to adjust using the least squares method mathematical expressions that allow interpolation of geoid heights. The objective of this study is to present an alternative method to interpolate geoid heights based on the technique of Artificial Neural Networks (ANNs). The study area is the Brazilian state of São Paulo, and for training the ANN the authors have used geoid height information from the EGM08 gravity model with a grid spacing of 10 minutes of arc. The efficiency of the model was tested at 157 points with known geoid heights distributed across the study area. The results were also compared with the Brazilian Geoid Model (MAPGEO2004). Based on those 157 benchmarks it was possible to verify that the model generated by ANNs provided a mean absolute error of 0.24 m in obtaining a geoid height value. Statistical tests have shown that there was no difference between the means from known geoid heights and geoid heights provided by the neural model for a significance level of 5%. It was also found that ANNs provided an improvement of 2.7 times in geoid height estimates when compared with the MAPGEO2004 geoid model.
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Two remote-sensing optical algorithms for the retrieval of the water quality components (WQCs) in the Albemarle-Pamlico Estuarine System (APES) were developed and validated for chlorophyll a (Chl). Both algorithms were semi-empirical because they incorporated some elements of optical processes in the atmosphere, water,

Two remote-sensing optical algorithms for the retrieval of the water quality components (WQCs) in the Albemarle-Pamlico Estuarine System (APES) were developed and validated for chlorophyll a (Chl). Both algorithms were semi-empirical because they incorporated some elements of optical processes in the atmosphere, water, and air/water interface. One incorporated a very simple atmospheric correction and modified quasi-single-scattering approximation (QSSA) for estimating the spectral Gordon’s parameter, and the second estimated WQCs directly from the top of atmosphere satellite radiance without atmospheric corrections. A modified version of the Global Meteorological Database for Solar Energy and Applied Meteorology (METEONORM) was used to estimate directional atmospheric transmittances. The study incorporated insitu Chl data from the Ferry-Based Monitoring (FerryMon) program collected in the Neuse River Estuary (n = 633) and Pamlico Sound (n = 362), along with Medium Resolution Imaging Spectrometer (MERIS) satellite imagery collected (2006–2009) across the APES; providing quasi-coinciding samples for Chl algorithm development and validation. Results indicated a coefficient of determination (R2) of 0.70 and mean-normalized root-mean-squares errors (NRMSE) of 52% in the Neuse River Estuary and R2 = 0.44 (NRMSE = 75 %) in the Pamlico Sound—without atmospheric corrections. The simple atmospheric correction tested provided on performance improvements. Algorithm performance demonstrated the potential for supporting long-term operational WQCs satellite monitoring in the APES.
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The Ice, Cloud and Land Elevation Satellite (ICESat) laser altimetry mission from 2003 to 2008 provided an important dataset for elevation measurements. The quality of GLAS/ICESat (Geoscience Laser Altimeter System) data was investigated for Lake Leman in Switzerland and France by comparing laser

The Ice, Cloud and Land Elevation Satellite (ICESat) laser altimetry mission from 2003 to 2008 provided an important dataset for elevation measurements. The quality of GLAS/ICESat (Geoscience Laser Altimeter System) data was investigated for Lake Leman in Switzerland and France by comparing laser data to hydrological gauge water levels. The correction of GLAS/ICESat waveform saturation successfully improved the quality of water elevation data. First, the ICESat elevations and waveforms corresponding to water footprints across the transition from the land to water were analyzed. Water elevations (2 to 10 measurements) following the land-water transition are often of lesser quality. The computed accuracy for the ICESat elevation measurements is approximately 5 cm, excluding transitions footprints, and 15 cm, including these footprints. Second, the accuracy of ICESat elevation was studied using data acquired on French rivers with a width greater than the size of the ICESat footprint. The obtained root mean square error (RMSE) for ICESat elevations in regard to French rivers was 1.14 m (bias = 0.07 m; standard deviation = 1.15 m), which indicates that small rivers could not be monitored using ICESat with acceptable accuracy due to land-water transition sensor inertia.
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This paper introduces the automated characterization of real estate (real property) for Internet mapping. It proposes a processing framework to achieve this task from vertical aerial photography and associated property information. A demonstration of the feasibility of an automated solution builds on test

This paper introduces the automated characterization of real estate (real property) for Internet mapping. It proposes a processing framework to achieve this task from vertical aerial photography and associated property information. A demonstration of the feasibility of an automated solution builds on test data from the Austrian City of Graz. Information is extracted from vertical aerial photography and various data products derived from that photography in the form of a true orthophoto, a dense digital surface model and digital terrain model, and a classification of land cover. Maps of cadastral property boundaries aid in defining real properties. Our goal is to develop a table for each property with descriptive numbers about the buildings, their dimensions, number of floors, number of windows, roof shapes, impervious surfaces, garages, sheds, vegetation, presence of a basement floor, and other descriptors of interest for each and every property of a city. From aerial sources, at a pixel size of 10 cm, we show that we have obtained positional accuracies in the range of a single pixel, an accuracy of areas in the 10% range, floor counts at an accuracy of 93% and window counts at 86% accuracy. We also introduce 3D point clouds of facades and their creation from vertical aerial photography, and how these point clouds can support the definition of complex facades.
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The trend of land cover (LC) and land cover change (LCC), both in time and space, was investigated at the Simen Mountains National Park (SMNP), a World Heritage Site located in northern Ethiopia, between 1984 and 2003 using Geographical Information System (GIS) and

The trend of land cover (LC) and land cover change (LCC), both in time and space, was investigated at the Simen Mountains National Park (SMNP), a World Heritage Site located in northern Ethiopia, between 1984 and 2003 using Geographical Information System (GIS) and remote sensing (RS). The objective of the study was to generate spatially and temporally quantified information on land cover dynamics, providing the basis for policy/decision makers and resource managers to facilitate biodiversity conservation, including wild animals. Two satellite images (Landsat TM of 1984 and Landsat ETM+ of 2003) were acquired and supervised classification was used to categorize LC types. Ground Control Points were obtained in field condition for georeferencing and accuracy assessment. The results showed an increase in the areas of pure forest (Erica species dominated) and shrubland but a decrease in the area of agricultural land over the 20 years. The overall accuracy and the Kappa value of classification results were 88 and 85%, respectively. The spatial setting of the LC classes was heterogeneous and resulted from the biophysical nature of SMNP and anthropogenic activities. Further studies are suggested to evaluate the existing LC and LCC in connection with wildlife habitat, conservation and management of SMNP.
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The Global Climate Observing System (GCOS) was established in 1992 to ensure that the observations necessary to address climate-related issues are defined, obtained and made available, to all potential users. The Swiss GCOS Office at the Federal Office of Meteorology and Climatology MeteoSwiss

The Global Climate Observing System (GCOS) was established in 1992 to ensure that the observations necessary to address climate-related issues are defined, obtained and made available, to all potential users. The Swiss GCOS Office at the Federal Office of Meteorology and Climatology MeteoSwiss has the task of coordinating all climate relevant measurements in Switzerland (GCOS Switzerland). As such, the Swiss GCOS Office also fosters the exploration of new measurement techniques and methods, in particular through the use of satellite-based data, to complement the long-term in situ observations in Switzerland. In this paper, the role of satellites is presented for climatological studies of atmospheric and terrestrial Essential Climate Variables in Switzerland. For the atmospheric domain, the 10-year climatology March 2000–February 2010 of cloud cover from MODIS is shown for Switzerland, in low (1° × 1°) and high (0.05° × 0.05°) resolution, and compared to ground-based synop observations. For the terrestrial domain, the satellite-derived Swiss glacier inventory from 1998/99 and the new Alpine-wide inventory from 2003 is presented along with area changes derived from a comparison with previous inventories.
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This study evaluates a methodology for using IKONOS stereo imagery to determine the height and position of buildings in dense residential areas. The method was tested on three selected sites in an area of 8.5 km long by 7 km wide and covered

This study evaluates a methodology for using IKONOS stereo imagery to determine the height and position of buildings in dense residential areas. The method was tested on three selected sites in an area of 8.5 km long by 7 km wide and covered by two overlapping (97% overlap) IKONOS images. The images were oriented using rational function models in addition to ground control points. Buildings were identified using an algorithm that utilized the Digital Surface Model (DSM) extracted from the images in addition to the image spectral properties. A digital terrain model was used with the DSM created from the IKONOS stereo imagery to compute building heights. Positional accuracy and building heights were evaluated using corner coordinates extracted from topographic maps and surveyed building heights. The results showed that the average building detection percentage for the test area was 82.6% with an average missing factor of 0.16. When the image rational polynomial coefficients were used to build the image model, results showed a horizontal accuracy of 2.42 and 2.39 m Root Mean Square Error (RMSE) for the easting and northing coordinates, respectively. When ground control points were used, the results improved to the sub-meter level. Differences between building heights extracted from the image model and the corresponding heights obtained through traditional ground surveying had a RMSE of 1.05 m.
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Novel radar satellite missions also include sensors operating in X-band at very high resolution. The presented study reports methodologies, algorithms and results on forest assessment utilizing such X-band satellite images, namely from TerraSAR-X and COSMO-SkyMed sensors. The proposed procedures cover advanced stereo-radargrammetric and

Novel radar satellite missions also include sensors operating in X-band at very high resolution. The presented study reports methodologies, algorithms and results on forest assessment utilizing such X-band satellite images, namely from TerraSAR-X and COSMO-SkyMed sensors. The proposed procedures cover advanced stereo-radargrammetric and interferometric data processing, as well as image segmentation and image classification. A core methodology is the multi-image matching concept for digital surface modeling based on geometrically constrained matching. Validation of generated surface models is made through comparison with LiDAR data, resulting in a standard deviation height error of less than 2 meters over forest. Image classification of forest regions is then based on X-band backscatter information, a canopy height model and interferometric coherence information yielding a classification accuracy above 90%. Such information is then directly used to extract forest border lines. High resolution X-band sensors deliver imagery that can be used for automatic forest assessment on a large scale.
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Maps of irrigated areas are essential for Ghana’s agricultural development. The goal of this research was to map irrigated agricultural areas and explain methods and protocols using remote sensing. Landsat Enhanced Thematic Mapper (ETM+) data and time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to map irrigated agricultural areas as well as other land use/land cover (LULC) classes, for Ghana. Temporal variations in the normalized difference vegetation index (NDVI) pattern obtained in the LULC class were used to identify irrigated and non-irrigated areas. First, the temporal variations in NDVI pattern were found to be more consistent in long-duration irrigated crops than with short-duration rainfed crops due to more assured water supply for irrigated areas. Second, surface water availability for irrigated areas is dependent on shallow dug-wells (on river banks) and dug-outs (in river bottoms) that affect the timing of crop sowing and growth stages, which was in turn reflected in the seasonal NDVI pattern. A decision tree approach using Landsat 30 m one time data fusion with MODIS 250 m time-series data was adopted to classify, group, and label classes. Finally, classes were tested and verified using ground truth data and national statistics. Fuzzy classification accuracy assessment for the irrigated classes varied between 67 and 93%. An irrigated area derived from remote sensing (32,421 ha) was 20–57% higher than irrigated areas reported by Ghana’s Irrigation Development Authority (GIDA). This was because of the uncertainties involved in factors such as: (a) absence of shallow irrigated area statistics in GIDA statistics, (b) non-clarity in the irrigated areas in its use, under-development, and potential for development in GIDA statistics, (c) errors of omissions and commissions in the remote sensing approach, and (d) comparison involving widely varying data types, methods, and approaches used in determining irrigated area statistics using GIDA and remote sensing. Extensive field campaigns to help in better classification and validation of irrigated areas using high (30 m ) to very high (<5 m) resolution remote sensing data that are fused with multi temporal data like MODIS are the way forward. This is especially true in accounting for small yet contiguous patches of irrigated areas from dug-wells and dug-outs.
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